Spaces:
Running
Running
File size: 24,537 Bytes
616e7e7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "35d8939e-909d-45d8-bcf9-0ff1dccacfdf",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['bert.encoder.layer.6.output.LayerNorm.weight', 'bert.encoder.layer.6.attention.self.query.weight', 'bert.encoder.layer.3.attention.output.LayerNorm.bias', 'bert.encoder.layer.4.attention.self.value.bias', 'bert.encoder.layer.2.attention.self.value.bias', 'bert.encoder.layer.10.intermediate.dense.bias', 'bert.encoder.layer.3.intermediate.dense.bias', 'bert.encoder.layer.6.attention.self.value.weight', 'bert.encoder.layer.11.output.dense.bias', 'bert.encoder.layer.3.attention.self.value.bias', 'bert.encoder.layer.7.attention.self.value.bias', 'bert.encoder.layer.2.attention.output.dense.weight', 'bert.encoder.layer.11.attention.output.dense.weight', 'bert.encoder.layer.6.output.dense.bias', 'bert.encoder.layer.6.attention.output.dense.bias', 'bert.encoder.layer.4.output.LayerNorm.weight', 'bert.encoder.layer.9.output.dense.weight', 'bert.encoder.layer.9.attention.self.key.bias', 'bert.encoder.layer.3.attention.self.key.weight', 'bert.encoder.layer.3.intermediate.dense.weight', 'bert.encoder.layer.8.output.LayerNorm.weight', 'cls.seq_relationship.bias', 'bert.encoder.layer.6.attention.self.value.bias', 'bert.encoder.layer.10.output.LayerNorm.bias', 'bert.encoder.layer.10.attention.output.LayerNorm.bias', 'bert.encoder.layer.8.attention.self.key.bias', 'bert.encoder.layer.3.attention.self.query.weight', 'bert.encoder.layer.8.intermediate.dense.weight', 'bert.encoder.layer.8.attention.output.LayerNorm.bias', 'bert.encoder.layer.7.attention.output.dense.weight', 'bert.encoder.layer.9.attention.self.query.bias', 'bert.encoder.layer.2.output.dense.bias', 'bert.encoder.layer.6.attention.self.key.bias', 'bert.encoder.layer.4.attention.self.query.weight', 'bert.encoder.layer.2.attention.self.query.weight', 'bert.encoder.layer.11.attention.self.query.weight', 'bert.encoder.layer.3.attention.output.dense.weight', 'bert.encoder.layer.11.attention.output.LayerNorm.bias', 'bert.encoder.layer.10.attention.self.key.weight', 'bert.encoder.layer.3.attention.self.value.weight', 'bert.encoder.layer.5.attention.self.key.bias', 'bert.encoder.layer.5.intermediate.dense.bias', 'bert.encoder.layer.7.attention.self.key.weight', 'bert.encoder.layer.5.attention.self.value.weight', 'bert.encoder.layer.2.attention.output.dense.bias', 'bert.encoder.layer.2.output.dense.weight', 'bert.encoder.layer.6.attention.output.dense.weight', 'bert.encoder.layer.2.intermediate.dense.bias', 'bert.encoder.layer.9.attention.self.value.bias', 'bert.encoder.layer.6.intermediate.dense.bias', 'bert.encoder.layer.9.attention.output.dense.bias', 'bert.encoder.layer.7.attention.self.query.weight', 'bert.encoder.layer.8.attention.self.value.bias', 'bert.encoder.layer.4.attention.self.key.bias', 'bert.pooler.dense.bias', 'bert.encoder.layer.10.attention.output.dense.bias', 'bert.encoder.layer.5.output.LayerNorm.weight', 'cls.seq_relationship.weight', 'bert.encoder.layer.11.intermediate.dense.weight', 'bert.encoder.layer.2.attention.self.key.bias', 'bert.encoder.layer.10.attention.output.LayerNorm.weight', 'bert.encoder.layer.10.output.dense.bias', 'bert.encoder.layer.10.intermediate.dense.weight', 'bert.encoder.layer.4.intermediate.dense.weight', 'bert.encoder.layer.3.attention.self.key.bias', 'bert.encoder.layer.5.attention.self.query.weight', 'bert.encoder.layer.9.intermediate.dense.weight', 'bert.pooler.dense.weight', 'bert.encoder.layer.7.attention.output.LayerNorm.bias', 'bert.encoder.layer.11.output.LayerNorm.weight', 'bert.encoder.layer.5.attention.output.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'bert.encoder.layer.10.attention.self.value.bias', 'bert.encoder.layer.4.attention.self.query.bias', 'bert.encoder.layer.3.attention.self.query.bias', 'bert.encoder.layer.10.output.LayerNorm.weight', 'bert.encoder.layer.10.attention.self.key.bias', 'bert.encoder.layer.8.attention.self.value.weight', 'bert.encoder.layer.4.output.dense.bias', 'bert.encoder.layer.7.attention.self.key.bias', 'bert.encoder.layer.8.intermediate.dense.bias', 'bert.encoder.layer.7.intermediate.dense.weight', 'bert.encoder.layer.2.attention.self.key.weight', 'bert.encoder.layer.4.attention.output.dense.bias', 'bert.encoder.layer.6.output.dense.weight', 'bert.encoder.layer.8.attention.output.LayerNorm.weight', 'bert.encoder.layer.11.output.LayerNorm.bias', 'bert.encoder.layer.10.output.dense.weight', 'bert.encoder.layer.4.attention.output.LayerNorm.bias', 'bert.encoder.layer.11.output.dense.weight', 'bert.encoder.layer.8.output.dense.weight', 'bert.encoder.layer.5.attention.self.value.bias', 'bert.encoder.layer.4.intermediate.dense.bias', 'bert.encoder.layer.5.attention.self.key.weight', 'bert.encoder.layer.4.attention.self.key.weight', 'bert.encoder.layer.7.attention.self.query.bias', 'bert.encoder.layer.10.attention.self.query.weight', 'bert.encoder.layer.5.output.dense.bias', 'bert.encoder.layer.5.attention.output.dense.weight', 'bert.encoder.layer.7.output.dense.bias', 'bert.embeddings.token_type_embeddings.weight', 'bert.encoder.layer.8.output.dense.bias', 'bert.encoder.layer.7.attention.output.LayerNorm.weight', 'bert.encoder.layer.6.attention.self.key.weight', 'bert.encoder.layer.11.attention.output.LayerNorm.weight', 'bert.encoder.layer.7.output.LayerNorm.bias', 'bert.encoder.layer.9.attention.output.LayerNorm.weight', 'bert.encoder.layer.3.output.dense.bias', 'bert.encoder.layer.8.attention.self.query.bias', 'bert.encoder.layer.6.attention.self.query.bias', 'bert.encoder.layer.4.attention.output.dense.weight', 'bert.encoder.layer.6.intermediate.dense.weight', 'bert.encoder.layer.8.attention.output.dense.bias', 'bert.encoder.layer.10.attention.self.query.bias', 'bert.encoder.layer.8.attention.output.dense.weight', 'bert.encoder.layer.9.attention.output.dense.weight', 'bert.encoder.layer.5.output.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'bert.encoder.layer.9.attention.self.query.weight', 'bert.encoder.layer.2.attention.output.LayerNorm.bias', 'bert.encoder.layer.4.attention.self.value.weight', 'bert.encoder.layer.6.output.LayerNorm.bias', 'bert.encoder.layer.10.attention.output.dense.weight', 'bert.encoder.layer.5.attention.self.query.bias', 'bert.encoder.layer.3.output.dense.weight', 'bert.encoder.layer.2.output.LayerNorm.weight', 'bert.encoder.layer.4.output.LayerNorm.bias', 'bert.encoder.layer.9.attention.self.value.weight', 'bert.encoder.layer.6.attention.output.LayerNorm.bias', 'bert.encoder.layer.11.attention.output.dense.bias', 'bert.encoder.layer.2.attention.output.LayerNorm.weight', 'bert.encoder.layer.7.output.LayerNorm.weight', 'bert.encoder.layer.2.output.LayerNorm.bias', 'bert.encoder.layer.3.output.LayerNorm.bias', 'cls.predictions.decoder.weight', 'bert.encoder.layer.5.attention.output.LayerNorm.weight', 'bert.encoder.layer.2.intermediate.dense.weight', 'bert.encoder.layer.11.attention.self.key.weight', 'bert.encoder.layer.11.attention.self.value.weight', 'bert.encoder.layer.9.intermediate.dense.bias', 'bert.encoder.layer.11.intermediate.dense.bias', 'bert.encoder.layer.11.attention.self.key.bias', 'bert.encoder.layer.2.attention.self.value.weight', 'bert.encoder.layer.3.output.LayerNorm.weight', 'bert.encoder.layer.9.output.LayerNorm.bias', 'bert.encoder.layer.5.intermediate.dense.weight', 'bert.encoder.layer.8.output.LayerNorm.bias', 'bert.encoder.layer.9.output.LayerNorm.weight', 'bert.encoder.layer.7.attention.self.value.weight', 'bert.encoder.layer.9.output.dense.bias', 'bert.encoder.layer.7.intermediate.dense.bias', 'bert.encoder.layer.6.attention.output.LayerNorm.weight', 'bert.encoder.layer.8.attention.self.query.weight', 'bert.encoder.layer.9.attention.self.key.weight', 'bert.encoder.layer.4.output.dense.weight', 'bert.encoder.layer.2.attention.self.query.bias', 'bert.encoder.layer.9.attention.output.LayerNorm.bias', 'bert.encoder.layer.3.attention.output.dense.bias', 'bert.encoder.layer.7.output.dense.weight', 'bert.encoder.layer.10.attention.self.value.weight', 'bert.encoder.layer.8.attention.self.key.weight', 'bert.encoder.layer.11.attention.self.value.bias', 'cls.predictions.transform.LayerNorm.bias', 'bert.encoder.layer.3.attention.output.LayerNorm.weight', 'bert.encoder.layer.5.attention.output.dense.bias', 'bert.encoder.layer.4.attention.output.LayerNorm.weight', 'bert.encoder.layer.11.attention.self.query.bias', 'cls.predictions.transform.dense.bias', 'bert.encoder.layer.7.attention.output.dense.bias', 'bert.encoder.layer.5.output.LayerNorm.bias']\n",
"- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of BertModel were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['bert.encoder.layer.1.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.0.crossattention.self.query.bias', 'bert.encoder.layer.0.crossattention.output.dense.bias', 'bert.encoder.layer.1.crossattention.self.query.weight', 'bert.encoder.layer.0.crossattention.output.dense.weight', 'bert.encoder.layer.1.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.0.crossattention.self.key.weight', 'bert.encoder.layer.1.crossattention.output.dense.weight', 'bert.encoder.layer.0.crossattention.self.query.weight', 'bert.encoder.layer.0.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.1.crossattention.self.key.weight', 'bert.encoder.layer.0.crossattention.self.key.bias', 'bert.encoder.layer.1.crossattention.output.dense.bias', 'bert.encoder.layer.0.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.1.crossattention.self.value.weight', 'bert.encoder.layer.1.crossattention.self.value.bias', 'bert.encoder.layer.0.crossattention.self.value.bias', 'bert.encoder.layer.0.crossattention.self.value.weight', 'bert.encoder.layer.1.crossattention.self.query.bias', 'bert.encoder.layer.1.crossattention.self.key.bias']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/encoder/layer/0/crossattention/self/query is tied\n",
"/encoder/layer/0/crossattention/self/key is tied\n",
"/encoder/layer/0/crossattention/self/value is tied\n",
"/encoder/layer/0/crossattention/output/dense is tied\n",
"/encoder/layer/0/crossattention/output/LayerNorm is tied\n",
"/encoder/layer/0/intermediate/dense is tied\n",
"/encoder/layer/0/output/dense is tied\n",
"/encoder/layer/0/output/LayerNorm is tied\n",
"/encoder/layer/1/crossattention/self/query is tied\n",
"/encoder/layer/1/crossattention/self/key is tied\n",
"/encoder/layer/1/crossattention/self/value is tied\n",
"/encoder/layer/1/crossattention/output/dense is tied\n",
"/encoder/layer/1/crossattention/output/LayerNorm is tied\n",
"/encoder/layer/1/intermediate/dense is tied\n",
"/encoder/layer/1/output/dense is tied\n",
"/encoder/layer/1/output/LayerNorm is tied\n",
"--------------\n",
"/home/notebook/code/personal/S9049611/BLIP/output/blip_tagtotext_14m/blip_tagtotext_encoderdiv_tar_random_swin/caption_coco_finetune_tagparse_tagfinetune_threshold075_bceloss_tagsingle_5e6_epoch19_negative_1_05_pos_1_10/checkpoint_05.pth\n",
"--------------\n",
"load checkpoint from /home/notebook/code/personal/S9049611/BLIP/output/blip_tagtotext_14m/blip_tagtotext_encoderdiv_tar_random_swin/caption_coco_finetune_tagparse_tagfinetune_threshold075_bceloss_tagsingle_5e6_epoch19_negative_1_05_pos_1_10/checkpoint_05.pth\n",
"vit: swin_b\n",
"msg_v2 _IncompatibleKeys(missing_keys=['visual_encoder.layers.0.blocks.0.attn.relative_position_index', 'visual_encoder.layers.0.blocks.1.attn_mask', 'visual_encoder.layers.0.blocks.1.attn.relative_position_index', 'visual_encoder.layers.1.blocks.0.attn.relative_position_index', 'visual_encoder.layers.1.blocks.1.attn_mask', 'visual_encoder.layers.1.blocks.1.attn.relative_position_index', 'visual_encoder.layers.2.blocks.0.attn.relative_position_index', 'visual_encoder.layers.2.blocks.1.attn_mask', 'visual_encoder.layers.2.blocks.1.attn.relative_position_index', 'visual_encoder.layers.2.blocks.2.attn.relative_position_index', 'visual_encoder.layers.2.blocks.3.attn_mask', 'visual_encoder.layers.2.blocks.3.attn.relative_position_index', 'visual_encoder.layers.2.blocks.4.attn.relative_position_index', 'visual_encoder.layers.2.blocks.5.attn_mask', 'visual_encoder.layers.2.blocks.5.attn.relative_position_index', 'visual_encoder.layers.2.blocks.6.attn.relative_position_index', 'visual_encoder.layers.2.blocks.7.attn_mask', 'visual_encoder.layers.2.blocks.7.attn.relative_position_index', 'visual_encoder.layers.2.blocks.8.attn.relative_position_index', 'visual_encoder.layers.2.blocks.9.attn_mask', 'visual_encoder.layers.2.blocks.9.attn.relative_position_index', 'visual_encoder.layers.2.blocks.10.attn.relative_position_index', 'visual_encoder.layers.2.blocks.11.attn_mask', 'visual_encoder.layers.2.blocks.11.attn.relative_position_index', 'visual_encoder.layers.2.blocks.12.attn.relative_position_index', 'visual_encoder.layers.2.blocks.13.attn_mask', 'visual_encoder.layers.2.blocks.13.attn.relative_position_index', 'visual_encoder.layers.2.blocks.14.attn.relative_position_index', 'visual_encoder.layers.2.blocks.15.attn_mask', 'visual_encoder.layers.2.blocks.15.attn.relative_position_index', 'visual_encoder.layers.2.blocks.16.attn.relative_position_index', 'visual_encoder.layers.2.blocks.17.attn_mask', 'visual_encoder.layers.2.blocks.17.attn.relative_position_index', 'visual_encoder.layers.3.blocks.0.attn.relative_position_index', 'visual_encoder.layers.3.blocks.1.attn.relative_position_index'], unexpected_keys=[])\n"
]
}
],
"source": [
"from PIL import Image\n",
"import requests\n",
"import torch\n",
"from torchvision import transforms\n",
"from torchvision.transforms.functional import InterpolationMode\n",
"import ruamel_yaml as yaml\n",
"from models.tag2text import tag2text_caption\n",
"\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"\n",
"\n",
"\n",
"import gradio as gr\n",
"\n",
"image_size = 384\n",
"\n",
"\n",
"normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
" std=[0.229, 0.224, 0.225])\n",
"transform = transforms.Compose([transforms.Resize((image_size, image_size)),transforms.ToTensor(),normalize])\n",
"\n",
"\n",
"\n",
"#######Swin Version\n",
"pretrained = '/home/notebook/code/personal/S9049611/BLIP/output/blip_tagtotext_14m/blip_tagtotext_encoderdiv_tar_random_swin/caption_coco_finetune_tagparse_tagfinetune_threshold075_bceloss_tagsingle_5e6_epoch19_negative_1_05_pos_1_10/checkpoint_05.pth'\n",
"\n",
"config_file = 'configs/tag2text_caption.yaml'\n",
"config = yaml.load(open(config_file, 'r'), Loader=yaml.Loader)\n",
"\n",
"\n",
"model = tag2text_caption(pretrained=pretrained, image_size=image_size, vit=config['vit'], \n",
" vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'],\n",
" prompt=config['prompt'],config=config,threshold = 0.75 )\n",
"\n",
"model.eval()\n",
"model = model.to(device)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9772dc6f-680d-45a7-b39c-23770eb5258e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7860\n",
"Running on public URL: https://202e6e6a-b3d9-4c97.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://202e6e6a-b3d9-4c97.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'PIL.Image.Image'>\n",
"<class 'PIL.Image.Image'>\n"
]
}
],
"source": [
"\n",
"def inference(raw_image, model_n, input_tag, strategy):\n",
" if model_n == 'Image Captioning':\n",
" raw_image = raw_image.resize((image_size, image_size))\n",
" print(type(raw_image))\n",
" image = transform(raw_image).unsqueeze(0).to(device) \n",
" model.threshold = 0.75\n",
" if input_tag == '' or input_tag == 'none' or input_tag == 'None':\n",
" input_tag_list = None\n",
" else:\n",
" input_tag_list = []\n",
" input_tag_list.append(input_tag.replace(',',' | '))\n",
" # print(input_tag_list)\n",
" with torch.no_grad():\n",
" if strategy == \"Beam search\":\n",
" \n",
"\n",
" caption, tag_predict = model.generate(image,tag_input = input_tag_list, return_tag_predict = True)\n",
" if input_tag_list == None:\n",
" tag_1 = tag_predict\n",
" tag_2 = ['none']\n",
" else:\n",
" _, tag_1 = model.generate(image,tag_input = None, return_tag_predict = True)\n",
" tag_2 = tag_predict\n",
"\n",
" else:\n",
"\n",
" caption,tag_predict = model.generate(image, tag_input = input_tag_list,sample=True, top_p=0.9, max_length=20, min_length=5, return_tag_predict = True)\n",
" if input_tag_list == None:\n",
" tag_1 = tag_predict\n",
" tag_2 = ['none']\n",
" else:\n",
" _, tag_1 = model.generate(image,tag_input = None, return_tag_predict = True)\n",
" tag_2 = tag_predict\n",
" # return 'Caption: '+caption[0], 'Identified Tags:' + tag_predict[0]\n",
" # return tag_predict[0],caption[0]\n",
" return tag_1[0],tag_2[0],caption[0]\n",
" \n",
" # return 'caption: '+caption[0], tag_predict[0]\n",
"\n",
" else: \n",
" image_vq = transform_vq(raw_image).unsqueeze(0).to(device) \n",
" with torch.no_grad():\n",
" answer = model_vq(image_vq, question, train=False, inference='generate') \n",
" return 'answer: '+answer[0]\n",
" \n",
"inputs = [gr.inputs.Image(type='pil'),gr.inputs.Radio(choices=['Image Captioning'], type=\"value\", default=\"Image Captioning\", label=\"Task\"),gr.inputs.Textbox(lines=2, label=\"User Identified Tags (Optional, Enter with commas)\"),gr.inputs.Radio(choices=['Beam search','Nucleus sampling'], type=\"value\", default=\"Beam search\", label=\"Caption Decoding Strategy\")]\n",
"\n",
"# outputs = gr.outputs.Textbox(label=\"Output\")\n",
"# outputs = [gr.outputs.Textbox(label=\"Image Caption\"),gr.outputs.Textbox(label=\"Identified Tags\")]\n",
"outputs = [gr.outputs.Textbox(label=\"Model Identified Tags\"),gr.outputs.Textbox(label=\"User Identified Tags\"), gr.outputs.Textbox(label=\"Image Caption\") ]\n",
"\n",
"title = \"Tag2Text\"\n",
"\n",
"description = \"Gradio demo for Tag2Text: Guiding Language-Image Model via Image Tagging (Fudan University, OPPO Research Institute, International Digital Economy Academy).\"\n",
"\n",
"article = \"<p style='text-align: center'><a href='' target='_blank'>Tag2Text: Guiding Language-Image Model via Image Tagging</a> | <a href='' target='_blank'>Github Repo</a></p>\"\n",
"\n",
"demo = gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['images/COCO_val2014_000000551338.jpg',\"Image Captioning\",\"none\",\"Beam search\"], \n",
" ['images/COCO_val2014_000000551338.jpg',\"Image Captioning\",\"fence, sky\",\"Beam search\"],\n",
" # ['images/COCO_val2014_000000551338.jpg',\"Image Captioning\",\"grass\",\"Beam search\"],\n",
" ['images/COCO_val2014_000000483108.jpg',\"Image Captioning\",\"none\",\"Beam search\"],\n",
" ['images/COCO_val2014_000000483108.jpg',\"Image Captioning\",\"electric cable\",\"Beam search\"],\n",
" # ['images/COCO_val2014_000000483108.jpg',\"Image Captioning\",\"sky, train\",\"Beam search\"],\n",
" ['images/COCO_val2014_000000483108.jpg',\"Image Captioning\",\"track, train\",\"Beam search\"] , \n",
" ['images/COCO_val2014_000000483108.jpg',\"Image Captioning\",\"grass\",\"Beam search\"] \n",
" ])\n",
"\n",
"\n",
"demo.launch(share=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0da1f11b-e737-47a9-9b07-4e00c0835f63",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "73a4bb88-4200-4853-b1ba-34f0d4b6dc34",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3340a61f-c6bc-4ead-87ea-b26aa97b7a68",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "d49e3de4-c3f7-4835-90eb-d0d013fc0ffb",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "205e0317-1701-4afd-8d67-bedb6959f350",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "bf5301a5-80c5-4e44-835e-0160a97fef66",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "f63d7a06-7625-4e1c-855d-177971217a0d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "c929e566-1a6e-4280-96eb-c434ef9a35d0",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|